Linked Data. Department of Software Enginnering Faculty of Information Technology Czech Technical University in Prague Ivo Lašek, 2011

Transcription

1 Linked Data Department of Software Enginnering Faculty of Information Technology Czech Technical University in Prague Ivo Lašek, 2011 Semantic Web, MI-SWE, 11/2011, Lecture 9 Evropský sociální fond Praha & EU: Investujeme do vaší budoucnosti

2 Data Web Social Web Web 1998 Semantic Web Vision

3 Semantic Web Problems Big visions do not scale very well (e.g. web scale reasoning) It can not be done all at once

4 Linked Data

5 How can I get my dataset into the diagram? There must be resolvable (or URIs. They must resolve, with or without content negotiation, to RDF data in one of the popular RDF formats (RDFa, RDF/XML, Turtle, N- Triples). The dataset must contain at least 1000 triples. (Hence, your FOAF file most likely does not qualify.)

6 How can I get my dataset into the diagram? The dataset must be connected via RDF links to a dataset that is already in the diagram. This means, either your dataset must use URIs from the other dataset, or vice versa. We arbitrarily require at least 50 links. Access of the entire dataset must be possible via RDF crawling, via an RDF dump, or via a SPARQL endpoint.

7 Why Linked Data? Easier search for structured documents (use of URIs in RDF triples is similar to the use of URLs in classical links) Easier ontology matching Central authorities providing URIs for other data sources (e.g. DBpedia)

8 Linked Data Components URIs HTTP Resource Description Framework (RDF) Serialization formats (RDFa, RDF/XML, N3, Turtle, and others)

9 Web 2.0 APIs

10 Linked Data One big global data dataspace Common data format RDF Links between items within different data sources

11 RDF Data Model Graph-based data model URIs as global primary keys il:ivolasek rdf:type foaf:person Il:ivolasek =

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13 Linked Data Growth Christian Bizer: Linked Data LOD2 Summer School 2011

14 Semantic Data Sources

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17 DBPedia.org Extracting structured content from Wikipedia Information presented in RDF SPARQL endpoint: PREFIX dbprop: PREFIX db: < SELECT?who?work?genre WHERE { db:tokyo_mew_mew dbprop:illustrator?who.?work dbprop:author?who. OPTIONAL {?work dbprop:genre?genre }. }

18 January 2011 DBPedia statistics persons places species organizations music albums

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21 Freebase.com Knowledge base maintained by its community Own ontology of topics Ontology is user editable too Data is available for free for commercial and non-commercial use Data can be obtained via: API RDF endpoint Database dump

22 Freebase API Search queries: type=/people/person&indent=1 Results in JSON: { "status": "200 OK", "code": "/api/status/ok", "result": [ { "alias": [ "Abe Lincoln", "Honest Abe", "Abraham Lincoln", "abraham_lincoln" ], "article": { "id": "/m/0gzt" },

23 MQL query={"query":{"type":"/music/artist","name ":"The Police","album":[]}} Query { "query": { "type":"/music/artist", "name":"the Police", "album":[] } } Response { "type": "/music/artist", "name": "The Police", "album": [ "Outlandos d'amour", "Reggatta de Blanc", "Zenyatta Mondatta", "Ghost in the Machine", "Synchronicity", ] }

24 Semantic Web Browsers

25 Marbles

26 Zitgist

27 Semantic Search Engines

28 Document-Centric Semantic Search Engines

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30 Watson Parsing: Jena Repository: Jena? Reasoning: NO Keyword based search, SPARQL endpoint

31 Watson - Schema

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33 Swoogle Crawler: 3 Custom Crawlers Google Crawler (.rdf,.owl files) Focused Crawler Extracted URIs crawler Repository: Jena Index: Lucene Keyword based search

34 Swoogle Architecture

35 Data Analysis Classification of Semantic Web Documents Databases Makes assertions about individuals Ontologies Defines new terms Compute rank of SWDs Search ordering: Swoogle PR analogy to GPR

36 Entity-Centric Semantic Search Engines

37 Falcons Reasoning/Ontology matching: Falcon-ao Search ordering: TF-IDF in combination with popularity of ontologies Classes recommendation: Ordering according to their popularity Keyword search: Based on the indexed texts extracted from Virtual Documents

38 Falcon Screenshot

39 Linguistic Matching for Ontologies Virtual Documents (names, labels, comments) Levenshtein edit distance Vector Space Model + cosine similarity of VDs Graph Matching for Ontologies Similarity of two entities comes from the accumulation of similarities of involved statements Similarity of two statements comes from the accumulation of similarities of involved entities Falcon-ao

40 SWSE Crawler: MultiCrawler Repository: YARS2 storing quadruples (subject, predicate, object, context) Ontology matching: URIs, IFPs Reasoning: Future work (Scalable Authoritative OWL Reasoner - SAOR) Search ordering: ReConRank (Page Rank for Linked Data) Keyword based search: Lucene

41 SWSE Architecture Consolidate find synonymous identifiers Rank links-based analysis, scores assignment

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43 Sindice.com Crawler: SindiceBot robots.rdf semantic site maps crawling pingthesemanticweb.com 3 Indexes: URI index IFP index Keyword index

44 Sindice Architecture Crawler: Apache Nutch Hadoop MapReduce Reasoner: OWLIM Reasoner Keyword based search: Solr

45 Sindice Architecture

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